There are two main types of the permanent magnet (PM) synchronous motors used in electric vehicles (EVs): surface-mounted PM (SPM) motor and interior-mounted PM (IPM) motor [1]. For an SPM motor, the magnets of the motor are on the surface of the rotor. For an IPM motor, the magnets are buried inside the rotor. Due to the requirement of a large operating speed range for an electric vehicle, IPM motors are widely used in EV industry [2].
Efficiency is a very important issue for electric vehicles. In terms of IPM motors, improving efficiency requires 1) minimizing energy loss and 2) maximizing voltage utilization. From energy loss perspective, at each vehicle operating condition, a highest efficient reference command needs to be generated for control of an IPM motor. From voltage utilization standpoint, space vector pulse width modulation (SVPWM) instead of traditional sinusoidal pulse width modulation (SPWM) scheme is usually used for controlling an IPM motor through power electronic converters [3]. Further improvement of efficiency is achieved by extending SVPWM from linear modulation region to over or even six-step modulation regions [4].
Traditionally, an IPM motor is controlled by using the PI-based standard vector control method [5, 6], which consists of a torque controller and a rotor flux controller. However, recent studies have revealed that the conventional standard vector control for a PM motor has a decoupling inaccuracy issue [7], which has caused a great challenge to operate an IPM motor in over-modulation regions, in particular.
In order to overcome the challenge, the following control methods have been developed: H∞ control, fuzzy logic control [9], direct torque and flux control [10], proportional-integral-resonant control [11], sliding model control [12], predictive current control [13], and artificial neural network (ANN) control [14]. Particularly, the ANN controller in [14] replaced the speed controller to compensate the speed-tracking error. Although the ANN controller in [14] enhanced the tracking performance of the reference speed, it did not perform motor current control which is the most critical for control and operation of an IPM motor. Therefore, advanced control techniques based on approximate dynamic programming (ADP) and ANNs are desired that overcome challenges in the art.
ADP, employing the principle of optimality [15], is a very useful tool for solving optimal control problems of nonlinear systems [15, 16]. An effective approach, developed in recent years, is to use and train ANNs to realize the ADP-based control [17, 18]. ADP control based upon ANNs has been used for many nonlinear control applications, such as steering and controlling the speed of a two-axle vehicle [19], performing auto landing and control of an aircraft [21-23], controlling a turbogenerator [24], and tracking control with time delays [25]. However, as described herein, ANN-based ADP is used in IPM motor control, especially for controlling IPM motors from linear to over modulation regions.
Therefore, what is desired are improved control systems for controlling interior-mounted permanent magnet motors using an ANN-based ADP for an IPM.